CloudAcademy

Analyzing CPU vs GPU Performance for AWS Machine Learning

The hands-on lab is part of this learning path

Introduction to Machine Learning on AWS
course-steps 4 certification 1 lab-steps 2

Lab Steps

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Logging in to the Amazon Web Services Console
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Connecting to the Virtual Machine using SSH
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Starting a Jupyter Notebook Server
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Forwarding a Virtual Machine Port through an SSH Tunnel
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Setting Up the CPU vs. GPU Experiment
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Running the CPU vs. GPU Experiment
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Analyzing the CPU vs. GPU Experiment Results
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DifficultyBeginner
Duration45m
Students146

Description

Lab Overview

Graphics processing units (GPUs) and other hardware accelerators can dramatically reduce the time taken to train complex machine learning models. In this lab, you will take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform a CPU vs GPU performance analysis for Amazon Machine Learning. The instance is based on the AWS deep learning AMI that comes with many machine learning libraries pre-installed. You will create a Jupyter Notebook to write code and visualize results in a single document. The TensorFlow library is used for the CPU and GPU benchmark code.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Run Jupyter Notebook server and create Jupyter Notebooks for machine learning experiments
  • Configure an SSH tunnel to forward instance ports through an encrypted channel
  • Understand when GPUs can be advantageous in machine learning, and to what extent

Lab Prerequisites

You should be familiar with:

  • Working with Linux on the command-line
  • Knowledge of the Python programming language is beneficial, but not required

Lab Environment

Before completing the Lab instructions, the environment will look as follows:

After completing the Lab instructions, the environment should look similar to:

 

Updates

January 10th, 2019 - Added a validation Lab Step to check the work you perform in the Lab

About the Author

Students27360
Labs74
Courses7
Learning paths4

Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), Linux Foundation Certified System Administrator (LFCS), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.